Learning with ensembles
Implementing a simple majority vote classifier
  Combining different algorithms for classification with majority vote
Evaluating and tuning the ensemble classifierBagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary